1. Field
The present application relates to transforming metrology data from a semiconductor treatment system, and, more particularly, to transforming metrology data using multivariate analysis.
2. Related Art
In semiconductor manufacturing, metrology is increasingly utilized to ensure that individual process steps, as well as a sequence of process steps, adhere to design specifications. For example, metrology may be employed to identify instances of process drift, and provide data sufficient to establish control schemes for correcting such drift.
While critical dimension scanning electron microscopy (CD-SEM) metrology has been used in the past, the complexity of devices formed on semiconductor substrates and their ever-decreasing feature size (e.g., sub 100 nm technology nodes), coupled with increasingly sophisticated unit process and process integration schemes, have warranted the implementation of optical metrology. In addition to being non-destructive, in-line optical metrology, such as optical scatterometry, can be used for robust Advanced Process Control (APC).
In optical scatterometry, one application includes the use of periodic structures that are formed on semiconductor substrates in close proximity to the locations for the formation of operating structures in semiconductor devices. By determining the profile of the periodic structures, the quality of the fabrication process utilized to form the periodic structures, and by extension the operating structure of the semiconductor device proximate the periodic structures, can be evaluated.
In general, optical scatterometry involves illuminating the periodic grating with electromagnetic (EM) radiation, and measuring the resulting diffracted signal. The characteristics of the measured diffraction signal is typically compared to a library of pre-determined diffraction signals (i.e., simulated diffraction signals) that are associated with known profiles. When a match is made between the measured diffraction signal and one of the simulated diffraction signals, then the profile associated with the matching hypothetical diffraction signal is presumed to represent the profile of the periodic grating.
However, the process of generating a simulated diffraction signal typically involves performing a large number of complex calculations, which can be time consuming and computationally intensive. The amount of time and computational capability and capacity needed to generate simulated diffraction signals can limit the size and resolution (i.e., the number of entries and the increment between entries) of the library of simulated diffraction signals that can be generated. Moreover, the complexity of the measured diffraction signals (i.e., the amount of data) and the potential for the existence of noise can further hinder accurate correlation between measured diffraction signals and simulated diffraction signals. For example, differences in measured diffraction signals can often consist merely of a slight shift or small change in the shape of broad spectral features in the measured diffraction signals.